Overview

Dataset statistics

Number of variables20
Number of observations295534
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory45.1 MiB
Average record size in memory160.0 B

Variable types

Numeric20

Alerts

Time (s) is highly overall correlated with CO (ppm) and 2 other fieldsHigh correlation
CO (ppm) is highly overall correlated with Time (s) and 9 other fieldsHigh correlation
Temperature (C) is highly overall correlated with Time (s) and 2 other fieldsHigh correlation
Heater voltage (V) is highly overall correlated with R4 (MOhm) and 10 other fieldsHigh correlation
R1 (MOhm) is highly overall correlated with R2 (MOhm) and 8 other fieldsHigh correlation
R2 (MOhm) is highly overall correlated with R1 (MOhm) and 6 other fieldsHigh correlation
R3 (MOhm) is highly overall correlated with R1 (MOhm) and 5 other fieldsHigh correlation
R4 (MOhm) is highly overall correlated with Humidity (%r.h.) and 14 other fieldsHigh correlation
R5 (MOhm) is highly overall correlated with Heater voltage (V) and 13 other fieldsHigh correlation
R6 (MOhm) is highly overall correlated with Heater voltage (V) and 13 other fieldsHigh correlation
R7 (MOhm) is highly overall correlated with Heater voltage (V) and 13 other fieldsHigh correlation
R8 (MOhm) is highly overall correlated with CO (ppm) and 11 other fieldsHigh correlation
R9 (MOhm) is highly overall correlated with CO (ppm) and 12 other fieldsHigh correlation
R10 (MOhm) is highly overall correlated with CO (ppm) and 11 other fieldsHigh correlation
R11 (MOhm) is highly overall correlated with CO (ppm) and 11 other fieldsHigh correlation
R12 (MOhm) is highly overall correlated with CO (ppm) and 13 other fieldsHigh correlation
R13 (MOhm) is highly overall correlated with CO (ppm) and 12 other fieldsHigh correlation
R14 (MOhm) is highly overall correlated with CO (ppm) and 11 other fieldsHigh correlation
Humidity (%r.h.) is highly overall correlated with Time (s) and 3 other fieldsHigh correlation
Flow rate (mL/min) is highly skewed (γ1 = -101.9754107)Skewed
Time (s) is uniformly distributedUniform
Time (s) has unique valuesUnique
CO (ppm) has 32165 (10.9%) zerosZeros

Reproduction

Analysis started2022-12-20 08:30:43.262001
Analysis finished2022-12-20 08:32:32.626691
Duration1 minute and 49.36 seconds
Software versionpandas-profiling vv3.5.0
Download configurationconfig.json

Variables

Time (s)
Real number (ℝ)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct295534
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45466.252
Minimum0
Maximum90909.697
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:02:32.856803image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4549.2139
Q122740.01
median45480.967
Q368200.558
95-th percentile86367.118
Maximum90909.697
Range90909.697
Interquartile range (IQR)45460.548

Descriptive statistics

Standard deviation26247.285
Coefficient of variation (CV)0.57729158
Kurtosis-1.2004342
Mean45466.252
Median Absolute Deviation (MAD)22730.425
Skewness-0.00077195217
Sum1.3436823 × 1010
Variance6.8891995 × 108
MonotonicityStrictly increasing
2022-12-20T14:02:33.012181image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
60654.468 1
 
< 0.1%
60626.399 1
 
< 0.1%
60626.094 1
 
< 0.1%
60625.788 1
 
< 0.1%
60625.484 1
 
< 0.1%
60625.178 1
 
< 0.1%
60624.873 1
 
< 0.1%
60624.569 1
 
< 0.1%
60624.264 1
 
< 0.1%
Other values (295524) 295524
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
0.31 1
< 0.1%
0.619 1
< 0.1%
0.929 1
< 0.1%
1.238 1
< 0.1%
1.546 1
< 0.1%
1.856 1
< 0.1%
2.165 1
< 0.1%
2.475 1
< 0.1%
2.782 1
< 0.1%
ValueCountFrequency (%)
90909.697 1
< 0.1%
90909.392 1
< 0.1%
90909.087 1
< 0.1%
90908.783 1
< 0.1%
90908.476 1
< 0.1%
90908.171 1
< 0.1%
90907.866 1
< 0.1%
90907.56 1
< 0.1%
90907.253 1
< 0.1%
90906.948 1
< 0.1%

CO (ppm)
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct303
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.8982688
Minimum0
Maximum20
Zeros32165
Zeros (%)10.9%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:02:33.271560image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.44
median8.89
Q315.56
95-th percentile20
Maximum20
Range20
Interquartile range (IQR)11.12

Descriptive statistics

Standard deviation6.426852
Coefficient of variation (CV)0.64929051
Kurtosis-1.2333938
Mean9.8982688
Median Absolute Deviation (MAD)6.67
Skewness0.0092984047
Sum2925275
Variance41.304426
MonotonicityNot monotonic
2022-12-20T14:02:33.428971image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 32165
10.9%
6.67 29255
9.9%
17.78 29254
9.9%
15.56 29250
9.9%
8.89 29247
9.9%
2.22 29243
9.9%
4.44 29238
9.9%
13.33 29238
9.9%
11.11 29182
9.9%
20 29168
9.9%
Other values (293) 294
 
0.1%
ValueCountFrequency (%)
0 32165
10.9%
0.1465 1
 
< 0.1%
0.2198 1
 
< 0.1%
0.2842 1
 
< 0.1%
0.6616 1
 
< 0.1%
0.7814 1
 
< 0.1%
0.7936 1
 
< 0.1%
0.8991 1
 
< 0.1%
1.0777 1
 
< 0.1%
1.1988 1
 
< 0.1%
ValueCountFrequency (%)
20 29168
9.9%
19.9289 1
 
< 0.1%
19.5338 1
 
< 0.1%
19.4716 1
 
< 0.1%
19.3955 1
 
< 0.1%
19.2998 1
 
< 0.1%
18.8456 1
 
< 0.1%
18.7857 1
 
< 0.1%
18.7203 1
 
< 0.1%
18.1574 1
 
< 0.1%

Humidity (%r.h.)
Real number (ℝ)

Distinct21901
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.329201
Minimum16.38
Maximum72.38
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:02:33.606544image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum16.38
5-th percentile23.07
Q135.9
median46.59
Q355.07
95-th percentile64.25
Maximum72.38
Range56
Interquartile range (IQR)19.17

Descriptive statistics

Standard deviation12.351256
Coefficient of variation (CV)0.27247902
Kurtosis-0.74287121
Mean45.329201
Median Absolute Deviation (MAD)9.42
Skewness-0.18077741
Sum13396320
Variance152.55353
MonotonicityNot monotonic
2022-12-20T14:02:33.753953image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21.96 4397
 
1.5%
48.72 2564
 
0.9%
32.88 2547
 
0.9%
46.6 1792
 
0.6%
29.66 1661
 
0.6%
44.53 1638
 
0.6%
48.65 1599
 
0.5%
47.11 1575
 
0.5%
47.62 1509
 
0.5%
35.89 1416
 
0.5%
Other values (21891) 274836
93.0%
ValueCountFrequency (%)
16.38 605
0.2%
16.3802 1
 
< 0.1%
16.3803 1
 
< 0.1%
16.3804 1
 
< 0.1%
16.3805 1
 
< 0.1%
16.3807 1
 
< 0.1%
16.381 1
 
< 0.1%
16.4142 1
 
< 0.1%
16.4773 1
 
< 0.1%
16.5294 1
 
< 0.1%
ValueCountFrequency (%)
72.38 13
< 0.1%
72.3416 1
 
< 0.1%
72.2606 1
 
< 0.1%
72.1933 1
 
< 0.1%
72.1119 1
 
< 0.1%
72.0445 1
 
< 0.1%
71.9638 1
 
< 0.1%
71.9004 1
 
< 0.1%
71.9002 1
 
< 0.1%
71.9001 1
 
< 0.1%

Temperature (C)
Real number (ℝ)

Distinct7568
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.813559
Minimum23.58
Maximum26.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:02:33.915037image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum23.58
5-th percentile23.9
Q125.74
median26.06
Q326.22
95-th percentile26.5
Maximum26.7
Range3.12
Interquartile range (IQR)0.48

Descriptive statistics

Standard deviation0.73421031
Coefficient of variation (CV)0.028442816
Kurtosis1.8042832
Mean25.813559
Median Absolute Deviation (MAD)0.2
Skewness-1.6435974
Sum7628784.4
Variance0.53906478
MonotonicityNot monotonic
2022-12-20T14:02:34.065370image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26.02 27946
 
9.5%
26.14 27651
 
9.4%
26.06 21508
 
7.3%
26.1 21362
 
7.2%
25.98 13849
 
4.7%
26.46 12545
 
4.2%
26.26 11119
 
3.8%
26.22 9406
 
3.2%
26.38 9061
 
3.1%
26.18 7470
 
2.5%
Other values (7558) 133617
45.2%
ValueCountFrequency (%)
23.58 576
0.2%
23.5802 1
 
< 0.1%
23.5806 1
 
< 0.1%
23.5807 1
 
< 0.1%
23.5809 2
 
< 0.1%
23.581 1
 
< 0.1%
23.5811 1
 
< 0.1%
23.5818 1
 
< 0.1%
23.5824 1
 
< 0.1%
23.5825 2
 
< 0.1%
ValueCountFrequency (%)
26.7 650
0.2%
26.6998 1
 
< 0.1%
26.6996 1
 
< 0.1%
26.6981 1
 
< 0.1%
26.6978 1
 
< 0.1%
26.697 1
 
< 0.1%
26.6962 1
 
< 0.1%
26.6956 1
 
< 0.1%
26.6952 1
 
< 0.1%
26.6948 1
 
< 0.1%

Flow rate (mL/min)
Real number (ℝ)

Distinct11358
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean239.94466
Minimum0
Maximum273.7196
Zeros12
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:02:34.230175image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile239.7539
Q1239.8952
median239.9724
Q3240.04558
95-th percentile240.1811
Maximum273.7196
Range273.7196
Interquartile range (IQR)0.150375

Descriptive statistics

Standard deviation1.9271699
Coefficient of variation (CV)0.0080317267
Kurtosis11776.429
Mean239.94466
Median Absolute Deviation (MAD)0.0751
Skewness-101.97541
Sum70911805
Variance3.713984
MonotonicityNot monotonic
2022-12-20T14:02:34.374599image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
239.973 148
 
0.1%
239.979 140
 
< 0.1%
239.9726 139
 
< 0.1%
239.9747 138
 
< 0.1%
239.9791 137
 
< 0.1%
239.9764 137
 
< 0.1%
239.9679 137
 
< 0.1%
239.9588 135
 
< 0.1%
239.9771 134
 
< 0.1%
239.975 133
 
< 0.1%
Other values (11348) 294156
99.5%
ValueCountFrequency (%)
0 12
< 0.1%
36.7433 1
 
< 0.1%
39.4867 1
 
< 0.1%
42.6948 1
 
< 0.1%
44.5664 1
 
< 0.1%
95.0345 1
 
< 0.1%
102.8132 1
 
< 0.1%
110.3395 1
 
< 0.1%
115.8476 1
 
< 0.1%
117.4061 1
 
< 0.1%
ValueCountFrequency (%)
273.7196 1
< 0.1%
269.3056 1
< 0.1%
262.2621 1
< 0.1%
261.3533 1
< 0.1%
260.3501 1
< 0.1%
259.514 1
< 0.1%
258.0795 1
< 0.1%
257.1495 1
< 0.1%
255.1999 1
< 0.1%
254.2954 1
< 0.1%

Heater voltage (V)
Real number (ℝ)

Distinct1714
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3552383
Minimum0.199
Maximum0.901
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:02:34.535213image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.199
5-th percentile0.2
Q10.2
median0.2
Q30.207
95-th percentile0.899
Maximum0.901
Range0.702
Interquartile range (IQR)0.007

Descriptive statistics

Standard deviation0.2886625
Coefficient of variation (CV)0.81258834
Kurtosis-0.20501307
Mean0.3552383
Median Absolute Deviation (MAD)0
Skewness1.3374576
Sum104985
Variance0.08332604
MonotonicityNot monotonic
2022-12-20T14:02:34.688632image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2 159897
54.1%
0.899 24309
 
8.2%
0.201 9812
 
3.3%
0.898 5660
 
1.9%
0.9 5040
 
1.7%
0.202 2855
 
1.0%
0.2008 2607
 
0.9%
0.2001 2597
 
0.9%
0.2003 2591
 
0.9%
0.2002 2575
 
0.9%
Other values (1704) 77591
26.3%
ValueCountFrequency (%)
0.199 1130
0.4%
0.1991 1281
0.4%
0.1992 1368
0.5%
0.1993 1290
0.4%
0.1994 1375
0.5%
0.1995 1314
0.4%
0.1996 1331
0.5%
0.1997 1436
0.5%
0.1998 1395
0.5%
0.1999 1380
0.5%
ValueCountFrequency (%)
0.901 147
< 0.1%
0.9009 101
< 0.1%
0.9008 105
< 0.1%
0.9007 96
< 0.1%
0.9006 103
< 0.1%
0.9005 96
< 0.1%
0.9004 110
< 0.1%
0.9003 99
< 0.1%
0.9002 115
< 0.1%
0.9001 94
< 0.1%

R1 (MOhm)
Real number (ℝ)

Distinct8518
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.733125
Minimum0.0326
Maximum116.4568
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:02:34.854678image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0326
5-th percentile0.0786
Q10.4253
median2.1729
Q329.7827
95-th percentile70.2486
Maximum116.4568
Range116.4242
Interquartile range (IQR)29.3574

Descriptive statistics

Standard deviation24.035544
Coefficient of variation (CV)1.436405
Kurtosis0.79169117
Mean16.733125
Median Absolute Deviation (MAD)2.0913
Skewness1.3953001
Sum4945207.2
Variance577.70737
MonotonicityNot monotonic
2022-12-20T14:02:35.014986image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
71.3877 735
 
0.2%
71.9176 702
 
0.2%
72.5638 700
 
0.2%
73.7788 688
 
0.2%
70.2486 683
 
0.2%
73.1111 680
 
0.2%
68.0747 675
 
0.2%
69.6423 670
 
0.2%
75.0345 666
 
0.2%
70.7619 664
 
0.2%
Other values (8508) 288671
97.7%
ValueCountFrequency (%)
0.0326 1
 
< 0.1%
0.0329 2
< 0.1%
0.0332 2
< 0.1%
0.0333 1
 
< 0.1%
0.0335 2
< 0.1%
0.0336 2
< 0.1%
0.0338 4
< 0.1%
0.034 1
 
< 0.1%
0.0341 3
< 0.1%
0.0342 4
< 0.1%
ValueCountFrequency (%)
116.4568 1
 
< 0.1%
114.818 5
 
< 0.1%
113.4868 10
 
< 0.1%
111.9292 4
 
< 0.1%
110.6632 14
 
< 0.1%
109.181 13
 
< 0.1%
107.9756 28
< 0.1%
106.5634 30
< 0.1%
105.4143 43
< 0.1%
104.0673 46
< 0.1%

R2 (MOhm)
Real number (ℝ)

Distinct8227
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.821429
Minimum0.0548
Maximum135.8172
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:02:35.180172image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0548
5-th percentile0.1403
Q10.4975
median1.6205
Q333.9269
95-th percentile79.7171
Maximum135.8172
Range135.7624
Interquartile range (IQR)33.4294

Descriptive statistics

Standard deviation27.836241
Coefficient of variation (CV)1.4789653
Kurtosis0.2729895
Mean18.821429
Median Absolute Deviation (MAD)1.4788
Skewness1.3109236
Sum5562372.1
Variance774.85629
MonotonicityNot monotonic
2022-12-20T14:02:35.442375image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
81.1822 1123
 
0.4%
79.7171 1112
 
0.4%
80.5097 1109
 
0.4%
82.004 1069
 
0.4%
79.0683 1033
 
0.3%
77.677 1013
 
0.3%
78.3034 1006
 
0.3%
82.7015 998
 
0.3%
76.9383 938
 
0.3%
76.3332 924
 
0.3%
Other values (8217) 285209
96.5%
ValueCountFrequency (%)
0.0548 1
< 0.1%
0.0584 1
< 0.1%
0.0585 1
< 0.1%
0.0586 1
< 0.1%
0.059 2
< 0.1%
0.0592 1
< 0.1%
0.0595 1
< 0.1%
0.0602 1
< 0.1%
0.0603 1
< 0.1%
0.0606 2
< 0.1%
ValueCountFrequency (%)
135.8172 1
 
< 0.1%
133.9633 1
 
< 0.1%
124.4448 1
 
< 0.1%
119.5851 1
 
< 0.1%
117.8584 2
 
< 0.1%
116.4568 4
 
< 0.1%
114.818 5
 
< 0.1%
113.4868 2
 
< 0.1%
111.9292 7
 
< 0.1%
110.6632 26
< 0.1%

R3 (MOhm)
Real number (ℝ)

Distinct8211
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.711009
Minimum0.055
Maximum193.8919
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:02:35.612771image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.055
5-th percentile0.1126
Q10.6113
median5.2356
Q348.4112
95-th percentile83.0507
Maximum193.8919
Range193.8369
Interquartile range (IQR)47.7999

Descriptive statistics

Standard deviation29.529517
Coefficient of variation (CV)1.2453927
Kurtosis-0.48673394
Mean23.711009
Median Absolute Deviation (MAD)5.1211
Skewness0.9710413
Sum7007409.2
Variance871.99238
MonotonicityNot monotonic
2022-12-20T14:02:35.773977image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
84.6501 1231
 
0.4%
83.0507 1124
 
0.4%
85.3974 1123
 
0.4%
80.6932 1115
 
0.4%
86.3116 1104
 
0.4%
83.7703 1087
 
0.4%
82.2033 1086
 
0.4%
81.51 1061
 
0.4%
80.0247 1040
 
0.4%
87.0883 1036
 
0.4%
Other values (8201) 284527
96.3%
ValueCountFrequency (%)
0.055 1
 
< 0.1%
0.0556 1
 
< 0.1%
0.0561 1
 
< 0.1%
0.0562 1
 
< 0.1%
0.0564 1
 
< 0.1%
0.0568 3
< 0.1%
0.0572 1
 
< 0.1%
0.0574 1
 
< 0.1%
0.0578 1
 
< 0.1%
0.0579 2
< 0.1%
ValueCountFrequency (%)
193.8919 1
 
< 0.1%
122.1443 1
 
< 0.1%
115.7553 1
 
< 0.1%
112.8031 3
 
< 0.1%
111.2549 20
 
< 0.1%
109.9965 39
 
< 0.1%
108.5233 45
< 0.1%
107.3251 56
< 0.1%
105.9214 100
< 0.1%
104.7792 112
< 0.1%

R4 (MOhm)
Real number (ℝ)

Distinct7548
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.716693
Minimum0.0406
Maximum105.8281
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:02:35.950761image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0406
5-th percentile0.1024
Q12.1711
median22.6156
Q334.5923
95-th percentile51.1162
Maximum105.8281
Range105.7875
Interquartile range (IQR)32.4212

Descriptive statistics

Standard deviation17.596796
Coefficient of variation (CV)0.81028893
Kurtosis-0.86685049
Mean21.716693
Median Absolute Deviation (MAD)15.3906
Skewness0.30336642
Sum6418021.1
Variance309.64722
MonotonicityNot monotonic
2022-12-20T14:02:36.109046image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34.4447 1022
 
0.3%
33.64 1015
 
0.3%
33.8093 1000
 
0.3%
33.033 999
 
0.3%
34.7712 991
 
0.3%
32.898 987
 
0.3%
32.447 987
 
0.3%
34.2691 985
 
0.3%
35.7879 978
 
0.3%
34.1241 977
 
0.3%
Other values (7538) 285593
96.6%
ValueCountFrequency (%)
0.0406 1
 
< 0.1%
0.0413 2
< 0.1%
0.0415 4
< 0.1%
0.0418 1
 
< 0.1%
0.0419 2
< 0.1%
0.0424 1
 
< 0.1%
0.0425 2
< 0.1%
0.0426 1
 
< 0.1%
0.0427 1
 
< 0.1%
0.043 2
< 0.1%
ValueCountFrequency (%)
105.8281 1
 
< 0.1%
85.5816 1
 
< 0.1%
80.9098 3
 
< 0.1%
80.1281 1
 
< 0.1%
79.2097 7
 
< 0.1%
78.4601 15
< 0.1%
77.5789 16
< 0.1%
76.8594 20
< 0.1%
76.0133 27
< 0.1%
75.3221 33
< 0.1%

R5 (MOhm)
Real number (ℝ)

Distinct7880
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.401413
Minimum0.0487
Maximum131.5393
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:02:36.269892image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0487
5-th percentile0.1158
Q11.9206
median35.8481
Q354.0448
95-th percentile81.0192
Maximum131.5393
Range131.4906
Interquartile range (IQR)52.1242

Descriptive statistics

Standard deviation28.055098
Coefficient of variation (CV)0.83993747
Kurtosis-1.029783
Mean33.401413
Median Absolute Deviation (MAD)25.9094
Skewness0.28978705
Sum9871253.1
Variance787.08853
MonotonicityNot monotonic
2022-12-20T14:02:36.434425image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
49.4117 1416
 
0.5%
49.9804 1355
 
0.5%
48.8555 1352
 
0.5%
52.1043 1350
 
0.5%
48.6068 1343
 
0.5%
49.7203 1340
 
0.5%
51.7661 1331
 
0.5%
49.1574 1323
 
0.4%
48.3116 1320
 
0.4%
50.296 1320
 
0.4%
Other values (7870) 282084
95.4%
ValueCountFrequency (%)
0.0487 1
< 0.1%
0.049 1
< 0.1%
0.0495 1
< 0.1%
0.0496 1
< 0.1%
0.0501 1
< 0.1%
0.0502 2
< 0.1%
0.0505 2
< 0.1%
0.0507 1
< 0.1%
0.0508 2
< 0.1%
0.0511 2
< 0.1%
ValueCountFrequency (%)
131.5393 2
 
< 0.1%
129.7955 1
 
< 0.1%
126.116 2
 
< 0.1%
124.1949 3
 
< 0.1%
122.6378 9
 
< 0.1%
120.8197 8
 
< 0.1%
119.345 18
< 0.1%
117.6218 18
< 0.1%
116.223 22
< 0.1%
114.5874 36
< 0.1%

R6 (MOhm)
Real number (ℝ)

Distinct7842
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.380702
Minimum0.0464
Maximum145.1587
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:02:36.603622image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0464
5-th percentile0.1247
Q11.6412
median26.1979
Q352.1297
95-th percentile80.6531
Maximum145.1587
Range145.1123
Interquartile range (IQR)50.4885

Descriptive statistics

Standard deviation28.028145
Coefficient of variation (CV)0.92256409
Kurtosis-0.94692948
Mean30.380702
Median Absolute Deviation (MAD)24.9272
Skewness0.49168163
Sum8978530.4
Variance785.57691
MonotonicityNot monotonic
2022-12-20T14:02:36.759500image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
49.2798 1193
 
0.4%
49.6055 1187
 
0.4%
52.1297 1186
 
0.4%
50.2138 1181
 
0.4%
51.1251 1176
 
0.4%
48.4314 1173
 
0.4%
47.8646 1172
 
0.4%
49.8802 1171
 
0.4%
61.0677 1169
 
0.4%
51.7707 1158
 
0.4%
Other values (7832) 283768
96.0%
ValueCountFrequency (%)
0.0464 1
 
< 0.1%
0.0484 1
 
< 0.1%
0.0487 1
 
< 0.1%
0.0488 2
< 0.1%
0.0494 1
 
< 0.1%
0.0499 2
< 0.1%
0.0502 3
< 0.1%
0.0505 1
 
< 0.1%
0.0508 1
 
< 0.1%
0.0511 3
< 0.1%
ValueCountFrequency (%)
145.1587 1
 
< 0.1%
133.7912 1
 
< 0.1%
122.0913 1
 
< 0.1%
118.6302 4
 
< 0.1%
116.8232 2
 
< 0.1%
115.3585 12
 
< 0.1%
113.6483 14
 
< 0.1%
112.2611 13
 
< 0.1%
110.6402 41
< 0.1%
109.3244 62
< 0.1%

R7 (MOhm)
Real number (ℝ)

Distinct7778
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.598219
Minimum0.0536
Maximum125.3547
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:02:36.929042image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0536
5-th percentile0.123
Q12.0136
median35.0441
Q355.0846
95-th percentile82.0372
Maximum125.3547
Range125.3011
Interquartile range (IQR)53.071

Descriptive statistics

Standard deviation28.457897
Coefficient of variation (CV)0.84700611
Kurtosis-1.0854013
Mean33.598219
Median Absolute Deviation (MAD)26.7379
Skewness0.29762028
Sum9929416
Variance809.85188
MonotonicityNot monotonic
2022-12-20T14:02:37.092143image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
52.0732 1404
 
0.5%
51.1767 1360
 
0.5%
51.7898 1354
 
0.5%
49.4202 1350
 
0.5%
49.9924 1341
 
0.5%
50.2568 1338
 
0.5%
52.4174 1334
 
0.5%
52.7077 1334
 
0.5%
49.6787 1329
 
0.4%
51.4536 1327
 
0.4%
Other values (7768) 282063
95.4%
ValueCountFrequency (%)
0.0536 2
< 0.1%
0.0538 2
< 0.1%
0.0541 1
< 0.1%
0.0543 1
< 0.1%
0.0544 1
< 0.1%
0.0545 2
< 0.1%
0.0546 1
< 0.1%
0.0549 1
< 0.1%
0.055 1
< 0.1%
0.0551 2
< 0.1%
ValueCountFrequency (%)
125.3547 1
 
< 0.1%
116.9118 1
 
< 0.1%
115.5214 5
 
< 0.1%
113.8957 9
 
< 0.1%
112.5752 15
 
< 0.1%
111.0301 25
 
< 0.1%
109.7743 46
 
< 0.1%
108.304 81
< 0.1%
107.1083 106
< 0.1%
105.7075 139
< 0.1%

R8 (MOhm)
Real number (ℝ)

Distinct6209
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.319321
Minimum0.0326
Maximum106.9859
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:02:37.258746image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0326
5-th percentile0.0996
Q112.2391
median28.1511
Q343.5025
95-th percentile63.5216
Maximum106.9859
Range106.9533
Interquartile range (IQR)31.2634

Descriptive statistics

Standard deviation20.738531
Coefficient of variation (CV)0.73231031
Kurtosis-0.88538316
Mean28.319321
Median Absolute Deviation (MAD)15.3514
Skewness0.14845362
Sum8369322.3
Variance430.08667
MonotonicityNot monotonic
2022-12-20T14:02:37.528364image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
54.4724 1160
 
0.4%
51.311 1150
 
0.4%
52.3446 1140
 
0.4%
53.0355 1128
 
0.4%
50.3742 1126
 
0.4%
51.6712 1124
 
0.4%
54.073 1111
 
0.4%
49.7495 1107
 
0.4%
51.9752 1101
 
0.4%
49.415 1096
 
0.4%
Other values (6199) 284291
96.2%
ValueCountFrequency (%)
0.0326 1
 
< 0.1%
0.0332 1
 
< 0.1%
0.0337 1
 
< 0.1%
0.0338 4
< 0.1%
0.0339 1
 
< 0.1%
0.0341 2
< 0.1%
0.0344 2
< 0.1%
0.0346 1
 
< 0.1%
0.0347 1
 
< 0.1%
0.035 1
 
< 0.1%
ValueCountFrequency (%)
106.9859 1
 
< 0.1%
95.604 1
 
< 0.1%
93.4149 1
 
< 0.1%
92.4523 1
 
< 0.1%
91.3229 3
 
< 0.1%
90.4024 1
 
< 0.1%
89.3217 2
 
< 0.1%
88.4405 3
 
< 0.1%
87.4055 14
< 0.1%
86.5611 17
< 0.1%

R9 (MOhm)
Real number (ℝ)

Distinct6201
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.136113
Minimum0.029
Maximum91.5335
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:02:37.685853image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.029
5-th percentile0.0967
Q18.4872
median22.6925
Q337.2693
95-th percentile58.3888
Maximum91.5335
Range91.5045
Interquartile range (IQR)28.7821

Descriptive statistics

Standard deviation18.819441
Coefficient of variation (CV)0.7797213
Kurtosis-0.75820298
Mean24.136113
Median Absolute Deviation (MAD)14.5768
Skewness0.35928813
Sum7133041.9
Variance354.17137
MonotonicityNot monotonic
2022-12-20T14:02:37.838736image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0971 2682
 
0.9%
0.097 2621
 
0.9%
0.0972 2558
 
0.9%
0.0977 2466
 
0.8%
0.0973 2395
 
0.8%
0.0978 2350
 
0.8%
0.0968 2341
 
0.8%
0.0975 2334
 
0.8%
0.098 2299
 
0.8%
0.0976 2298
 
0.8%
Other values (6191) 271190
91.8%
ValueCountFrequency (%)
0.029 1
 
< 0.1%
0.0293 1
 
< 0.1%
0.0294 4
< 0.1%
0.0296 1
 
< 0.1%
0.0299 1
 
< 0.1%
0.03 2
< 0.1%
0.0301 3
< 0.1%
0.0302 2
< 0.1%
0.0303 3
< 0.1%
0.0304 4
< 0.1%
ValueCountFrequency (%)
91.5335 1
 
< 0.1%
90.4623 1
 
< 0.1%
80.1318 1
 
< 0.1%
79.4435 2
 
< 0.1%
76.5489 1
 
< 0.1%
75.7953 2
 
< 0.1%
75.1784 10
 
< 0.1%
74.4511 9
 
< 0.1%
73.8555 21
< 0.1%
73.1532 36
< 0.1%

R10 (MOhm)
Real number (ℝ)

Distinct6398
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.553647
Minimum0.0372
Maximum139.2366
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:02:37.994508image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0372
5-th percentile0.1182
Q17.7003
median24.1369
Q342.2414
95-th percentile64.6599
Maximum139.2366
Range139.1994
Interquartile range (IQR)34.5411

Descriptive statistics

Standard deviation21.299549
Coefficient of variation (CV)0.80213273
Kurtosis-0.82609108
Mean26.553647
Median Absolute Deviation (MAD)17.6932
Skewness0.397209
Sum7847505.4
Variance453.67079
MonotonicityNot monotonic
2022-12-20T14:02:38.146511image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1195 1683
 
0.6%
0.1197 1669
 
0.6%
0.1194 1664
 
0.6%
0.1193 1650
 
0.6%
0.1199 1637
 
0.6%
0.1198 1626
 
0.6%
0.1205 1591
 
0.5%
0.1201 1588
 
0.5%
0.1202 1521
 
0.5%
0.1191 1493
 
0.5%
Other values (6388) 279412
94.5%
ValueCountFrequency (%)
0.0372 1
 
< 0.1%
0.0375 2
< 0.1%
0.0377 1
 
< 0.1%
0.0378 3
< 0.1%
0.0379 1
 
< 0.1%
0.038 1
 
< 0.1%
0.0383 1
 
< 0.1%
0.0385 2
< 0.1%
0.0388 3
< 0.1%
0.039 2
< 0.1%
ValueCountFrequency (%)
139.2366 1
 
< 0.1%
105.9214 1
 
< 0.1%
98.8084 1
 
< 0.1%
93.6563 1
 
< 0.1%
90.6767 3
 
< 0.1%
89.8357 6
 
< 0.1%
88.8467 4
 
< 0.1%
88.0388 12
< 0.1%
87.0883 20
< 0.1%
86.3116 21
< 0.1%

R11 (MOhm)
Real number (ℝ)

Distinct6283
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.480566
Minimum0.0305
Maximum100.3335
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:02:38.319874image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0305
5-th percentile0.1078
Q110.7086
median27.7396
Q344.3081
95-th percentile64.8558
Maximum100.3335
Range100.303
Interquartile range (IQR)33.5995

Descriptive statistics

Standard deviation21.334453
Coefficient of variation (CV)0.7490881
Kurtosis-0.89338592
Mean28.480566
Median Absolute Deviation (MAD)16.5685
Skewness0.21008417
Sum8416975.7
Variance455.15889
MonotonicityNot monotonic
2022-12-20T14:02:38.479217image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1082 2375
 
0.8%
0.1081 2243
 
0.8%
0.1089 2223
 
0.8%
0.1084 2216
 
0.7%
0.1088 2165
 
0.7%
0.1086 2154
 
0.7%
0.1085 2142
 
0.7%
0.108 2053
 
0.7%
0.109 1995
 
0.7%
0.1078 1911
 
0.6%
Other values (6273) 274057
92.7%
ValueCountFrequency (%)
0.0305 1
 
< 0.1%
0.0312 2
< 0.1%
0.0313 3
< 0.1%
0.0315 1
 
< 0.1%
0.0316 2
< 0.1%
0.0317 2
< 0.1%
0.0318 2
< 0.1%
0.0319 4
< 0.1%
0.032 1
 
< 0.1%
0.0321 2
< 0.1%
ValueCountFrequency (%)
100.3335 1
 
< 0.1%
96.936 1
 
< 0.1%
90.1079 2
 
< 0.1%
89.1159 5
 
< 0.1%
88.3056 9
 
< 0.1%
87.3522 13
 
< 0.1%
86.5731 14
 
< 0.1%
85.6562 23
< 0.1%
84.9067 34
< 0.1%
84.0241 35
< 0.1%

R12 (MOhm)
Real number (ℝ)

Distinct6292
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.696788
Minimum0.0334
Maximum89.2954
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:02:38.642920image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0334
5-th percentile0.1075
Q19.8438
median26.8138
Q341.8191
95-th percentile57.6725
Maximum89.2954
Range89.262
Interquartile range (IQR)31.9753

Descriptive statistics

Standard deviation19.587231
Coefficient of variation (CV)0.73369241
Kurtosis-0.90441257
Mean26.696788
Median Absolute Deviation (MAD)15.4145
Skewness0.12739103
Sum7889808.6
Variance383.65961
MonotonicityNot monotonic
2022-12-20T14:02:38.795233image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1088 1249
 
0.4%
0.1087 1208
 
0.4%
50.6129 1190
 
0.4%
51.8182 1184
 
0.4%
49.4614 1183
 
0.4%
0.1086 1182
 
0.4%
52.7888 1173
 
0.4%
0.1084 1171
 
0.4%
48.3602 1170
 
0.4%
51.5393 1164
 
0.4%
Other values (6282) 283660
96.0%
ValueCountFrequency (%)
0.0334 2
< 0.1%
0.0337 1
 
< 0.1%
0.0338 1
 
< 0.1%
0.034 2
< 0.1%
0.0341 2
< 0.1%
0.0343 1
 
< 0.1%
0.0344 3
< 0.1%
0.0347 2
< 0.1%
0.0348 4
< 0.1%
0.0349 2
< 0.1%
ValueCountFrequency (%)
89.2954 1
 
< 0.1%
88.4834 1
 
< 0.1%
87.5281 6
 
< 0.1%
86.7475 2
 
< 0.1%
85.8287 3
 
< 0.1%
85.0777 17
 
< 0.1%
84.1934 20
< 0.1%
83.4702 27
< 0.1%
82.6184 48
< 0.1%
81.9217 40
< 0.1%

R13 (MOhm)
Real number (ℝ)

Distinct6375
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.492004
Minimum0.0334
Maximum86.5605
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:02:38.953082image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0334
5-th percentile0.1011
Q17.9663
median22.3557
Q337.0376
95-th percentile54.8616
Maximum86.5605
Range86.5271
Interquartile range (IQR)29.0713

Descriptive statistics

Standard deviation18.060618
Coefficient of variation (CV)0.76879853
Kurtosis-0.82114607
Mean23.492004
Median Absolute Deviation (MAD)14.6425
Skewness0.29650641
Sum6942685.9
Variance326.18592
MonotonicityNot monotonic
2022-12-20T14:02:39.103987image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1027 1218
 
0.4%
0.1029 1123
 
0.4%
0.103 1118
 
0.4%
0.1023 1102
 
0.4%
0.1026 1087
 
0.4%
0.1025 1084
 
0.4%
0.1039 1073
 
0.4%
0.1031 1017
 
0.3%
0.1022 1008
 
0.3%
0.1042 997
 
0.3%
Other values (6365) 284707
96.3%
ValueCountFrequency (%)
0.0334 1
 
< 0.1%
0.0336 1
 
< 0.1%
0.0337 2
< 0.1%
0.0338 1
 
< 0.1%
0.0339 2
< 0.1%
0.0341 1
 
< 0.1%
0.0344 3
< 0.1%
0.0346 2
< 0.1%
0.0348 2
< 0.1%
0.0349 2
< 0.1%
ValueCountFrequency (%)
86.5605 1
 
< 0.1%
81.016 1
 
< 0.1%
78.7567 2
 
< 0.1%
78.1157 1
 
< 0.1%
76.7411 2
 
< 0.1%
76.0113 2
 
< 0.1%
75.4135 8
< 0.1%
74.7083 9
< 0.1%
74.1305 9
< 0.1%
73.4487 13
< 0.1%

R14 (MOhm)
Real number (ℝ)

Distinct6250
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.557879
Minimum0.032
Maximum117.4239
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:02:39.256462image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.032
5-th percentile0.107
Q19.7269
median27.6855
Q347.0102
95-th percentile69.4831
Maximum117.4239
Range117.3919
Interquartile range (IQR)37.2833

Descriptive statistics

Standard deviation22.895443
Coefficient of variation (CV)0.77459695
Kurtosis-0.97631509
Mean29.557879
Median Absolute Deviation (MAD)18.8122
Skewness0.27734614
Sum8735358.4
Variance524.20132
MonotonicityNot monotonic
2022-12-20T14:02:39.415661image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.108 2321
 
0.8%
0.1074 2257
 
0.8%
0.1079 2251
 
0.8%
0.1082 2231
 
0.8%
0.1072 2161
 
0.7%
0.1075 2097
 
0.7%
0.1071 2088
 
0.7%
0.1077 2083
 
0.7%
0.1078 2014
 
0.7%
0.1083 1964
 
0.7%
Other values (6240) 274067
92.7%
ValueCountFrequency (%)
0.032 1
 
< 0.1%
0.0321 2
 
< 0.1%
0.0322 4
< 0.1%
0.0324 3
< 0.1%
0.0325 1
 
< 0.1%
0.0326 5
< 0.1%
0.0328 1
 
< 0.1%
0.0329 3
< 0.1%
0.033 6
< 0.1%
0.0332 3
< 0.1%
ValueCountFrequency (%)
117.4239 1
 
< 0.1%
93.4235 1
 
< 0.1%
90.595 1
 
< 0.1%
89.5776 3
 
< 0.1%
87.7697 1
 
< 0.1%
86.9716 3
 
< 0.1%
86.0327 4
 
< 0.1%
85.2654 12
< 0.1%
84.3623 15
< 0.1%
83.6241 22
< 0.1%

Interactions

2022-12-20T14:02:26.903119image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:16.270460image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:20.272588image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:23.912394image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:27.405340image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:31.066872image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:35.271235image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:39.256054image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:43.065311image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:48.024324image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:51.884294image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:55.234853image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:58.747236image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:02:02.337631image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:02:05.785321image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:02:09.350609image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:02:12.717280image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:02:16.415197image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:02:19.938950image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:02:23.399149image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:02:27.073653image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:16.475544image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:20.443236image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:24.066521image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:27.596794image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:31.231340image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:35.464101image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:39.429399image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:43.242439image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:48.208141image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:52.049119image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:55.392410image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:58.909843image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:02:02.505865image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:02:05.947510image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:02:09.507544image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:02:12.884434image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:02:16.581239image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:02:20.098894image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:02:23.561233image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:02:27.263334image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:16.646385image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:20.622107image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:24.241447image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:27.804122image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:31.417341image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:35.658165image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:39.624376image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:43.663280image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:48.398398image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:52.231683image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:55.573895image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:59.098670image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:02:02.684198image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:02:06.124814image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:02:09.685863image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:02:13.073126image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:02:16.766542image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:02:20.370692image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:02:23.743914image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:02:27.434623image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:16.809466image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:20.792090image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:24.401001image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:27.969114image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:31.586950image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:35.856287image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:39.802076image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:43.930930image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:48.580749image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:52.393950image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:55.736451image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:59.277677image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:02:02.850738image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:02:06.292835image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:02:09.848574image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:02:13.247666image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:02:16.972105image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:02:20.539091image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:02:23.912023image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:02:27.614840image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:16.968504image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:20.960620image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:24.667171image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:28.158938image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:31.763448image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:36.050952image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:39.995298image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:44.233039image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:48.772728image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:52.566897image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:55.906922image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:59.452537image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:02:03.029561image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:02:06.472359image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:02:10.015747image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:02:13.429173image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:02:17.146663image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:02:20.707051image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:02:24.095519image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:02:27.787697image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:17.126849image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:21.142389image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:24.844428image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:28.328047image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:31.943803image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:36.243283image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:40.173360image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:44.460218image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:48.960765image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:52.729946image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:56.072356image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:59.621678image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:02:03.198324image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:02:06.635576image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:02:10.219979image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:02:13.598405image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:02:17.319031image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:02:20.873928image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:02:24.265430image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:02:27.977879image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:17.296532image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:21.439135image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:25.034602image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:28.515988image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:32.195025image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:36.447362image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:40.360593image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:44.737965image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:49.163597image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:52.909240image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:56.363505image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:59.801477image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:02:03.378798image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:02:06.807490image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:02:10.395496image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:02:13.791379image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:02:17.501308image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:02:21.052298image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:02:24.445905image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:02:28.154591image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:17.462078image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:21.659168image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:25.202211image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:28.694937image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:32.426172image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:36.645656image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:40.540618image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:44.960158image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:49.349930image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:53.070467image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:56.520117image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:59.982749image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:02:03.550469image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:02:06.977450image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:02:10.564695image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:02:13.970116image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:02:17.659884image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:02:21.224479image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:02:24.614618image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:02:28.332248image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:17.615309image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:21.840918image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:25.370021image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:28.876897image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2022-12-20T14:01:38.884844image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:42.611180image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:47.653249image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:51.532525image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:54.904871image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:58.420672image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:02:02.002123image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:02:05.448259image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:02:08.947506image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:02:12.394048image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:02:16.039967image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:02:19.599935image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:02:23.068974image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:02:26.477949image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:02:30.264075image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:20.110140image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:23.741892image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:27.172876image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:30.892821image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:35.077921image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:39.067352image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:42.835243image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:47.838087image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:51.703899image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:55.067408image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:01:58.579773image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:02:02.155287image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:02:05.610817image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:02:09.114964image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:02:12.547779image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:02:16.207553image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:02:19.766888image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:02:23.230033image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:02:26.733049image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2022-12-20T14:02:39.687314image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Auto

The auto setting is an interpretable pairwise column metric of the following mapping:
  • Variable_type-Variable_type : Method, Range
  • Categorical-Categorical : Cramer's V, [0,1]
  • Numerical-Categorical : Cramer's V, [0,1] (using a discretized numerical column)
  • Numerical-Numerical : Spearman's ρ, [-1,1]
The number of bins used in the discretization for the Numerical-Categorical column pair can be changed using config.correlations["auto"].n_bins. The number of bins affects the granularity of the association you wish to measure.

This configuration uses the recommended metric for each pair of columns.
2022-12-20T14:02:39.943962image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-12-20T14:02:40.209891image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-12-20T14:02:40.476774image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-12-20T14:02:40.745445image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-12-20T14:02:30.555898image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-12-20T14:02:31.391376image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Time (s)CO (ppm)Humidity (%r.h.)Temperature (C)Flow rate (mL/min)Heater voltage (V)R1 (MOhm)R2 (MOhm)R3 (MOhm)R4 (MOhm)R5 (MOhm)R6 (MOhm)R7 (MOhm)R8 (MOhm)R9 (MOhm)R10 (MOhm)R11 (MOhm)R12 (MOhm)R13 (MOhm)R14 (MOhm)
00.0000.063.084425.3805242.49950.200461.410069.144856.972539.409767.369165.377169.684473.962360.466764.659962.989060.883157.034968.3837
10.3100.062.436625.4364242.07910.200053.489864.102066.083238.185365.361162.931162.671860.166462.270670.335669.432557.672554.861668.3837
20.6190.060.218025.6280241.85860.200049.511158.502260.196735.787958.384758.847661.307170.125460.466764.659962.498059.235055.894669.4831
30.9290.057.992225.8202241.63740.200044.235653.789558.937435.103858.741654.521755.404761.076964.183767.664667.869755.797254.483967.8952
41.2380.055.773726.0118241.41700.200042.270750.397254.132533.640051.766152.129755.793668.298360.466764.659961.225055.474955.180470.6179
51.5460.053.564526.2025241.19740.200039.197747.875453.827333.500153.681551.125152.707762.538560.466762.309268.350654.778553.804467.3181
61.8560.051.340926.3945240.97640.200036.702743.547850.358531.881050.562049.279852.073267.165460.071065.098160.379156.519553.501068.9791
72.1650.049.124526.5859240.75610.200033.277939.197749.520232.737551.157148.693549.420262.538560.466763.715966.283453.081152.205266.2847
82.4750.048.730026.6200240.69440.200031.897034.074446.560430.947347.779346.478849.992462.981660.071064.659959.927253.081153.804470.0976
92.7820.048.730026.6200240.66720.199828.384529.058345.576332.161649.980445.955748.313463.521658.763061.040165.295452.788850.969567.3181
Time (s)CO (ppm)Humidity (%r.h.)Temperature (C)Flow rate (mL/min)Heater voltage (V)R1 (MOhm)R2 (MOhm)R3 (MOhm)R4 (MOhm)R5 (MOhm)R6 (MOhm)R7 (MOhm)R8 (MOhm)R9 (MOhm)R10 (MOhm)R11 (MOhm)R12 (MOhm)R13 (MOhm)R14 (MOhm)
29552490906.9480.060.7325.980.00.20003.59341.77596.575321.524938.419233.040338.374555.636345.293258.149858.682553.735544.153665.2822
29552590907.2530.060.7325.980.00.20093.02821.57515.533521.454237.925330.998137.696454.073045.293255.516857.145254.099145.562871.2524
29552690907.5600.060.7325.980.00.20002.58701.41944.692520.413036.772028.975435.335555.636344.320758.149856.405850.936142.827263.8761
29552790907.8660.060.7325.980.00.20062.23171.29184.016119.892536.629027.958734.757352.656443.596757.728957.903852.156743.260664.8363
29552890908.1710.060.7325.980.00.20001.95391.19173.478719.562635.550125.855833.800752.344643.140554.816157.556553.081143.260663.8761
29552990908.4760.060.7325.980.00.20001.72841.10873.038619.671434.279223.905632.011350.374243.140555.196258.326051.818244.826169.4831
29553090908.7830.060.7325.980.00.20001.54701.03962.684818.482133.211122.405030.397051.975244.062558.937456.806851.818243.023263.8761
29553190909.0870.060.7325.980.00.20001.39830.98142.395817.784832.205720.678529.134050.031542.255655.906656.806852.156742.173963.3641
29553290909.3920.060.7325.980.00.20001.27520.93252.159917.430930.244218.774127.783149.415043.849655.906655.685150.936142.402262.4461
29553390909.6970.060.7325.980.00.20001.17280.88991.962416.682028.909717.213326.050950.374244.320755.196255.685150.346642.402262.4461